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Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
[article]
2020
arXiv
pre-print
Single-objective black box optimization (also known as zeroth-order optimization) is the process of minimizing a scalar objective f(x), given evaluations at adaptively chosen inputs x. In this paper, we consider multi-objective optimization, where f(x) outputs a vector of possibly competing objectives and the goal is to converge to the Pareto frontier. Quantitatively, we wish to maximize the standard hypervolume indicator metric, which measures the dominated hypervolume of the entire set of
arXiv:2006.04655v2
fatcat:fccjcytxy5cgpom4gbn43y4m2m